Utility Optimization of Federated Learning with Differential Privacy

Jianzhe Zhao, Keming Mao, Chenxi Huang, Yuyang Zeng and Shi Cheng

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-14

Abstract: Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:3344862

DOI: 10.1155/2021/3344862

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